Using Search Engine Data for Detection and Early Intervention in Suicide Prevention

使用搜索引擎数据进行自杀预防的检测和早期干预

• 批准号: 10616794
• 负责人: KATHERINE ANNE COMTOIS
• 金额: $ 85.96万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-05 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10616794
• 关键词: Acute; American; Behavior; Behavioral; Cardiovascular system; Caring; Cause of Death; Cessation of life; Characteristics; Classification; Clinical; Cohort Studies; Communities; Crossover Design; Data; Decision Making; Detection; Diagnostic Procedure; Early Diagnosis; Early Intervention; Environmental Exposure; Ethics; Event; Facebook; Family; Feeling suicidal; Foundations; Friends; Fright; Future; Healthcare; Healthcare Systems; Hour; Individual; Injury; Inpatients; Internet; Interview; Knowledge; Length; Machine Learning; Medical; Medicine; Mental disorders; Meta-Analysis; Methods; National Institute of Mental Health; Nature; Online Systems; Outcome; Participant; Pathway interactions; Patients; Pattern; Persons; Population; Populations at Risk; Prevention; Primary Care; Privatization; Process; Prospective Studies; Recommendation; Recording of previous events; Reporting; Research; Research Personnel; Research Priority; Retrospective Studies; Risk; Risk Factors; Services; Standardization; Suicide; Suicide attempt; Suicide prevention; Surveys; System; Testing; Time; Twitter; Validation; Work; care systems; cohort; cost; detection method; digital; follow-up; gradient boosting; high risk; ideation; improved; information seeking behavior; innovation; instrument; machine learning method; mortality; online intervention; predictive tools; prevention service; prognostic signature; prospective; random forest; recruit; screening; search engine; social media; social stigma; suicidal behavior; suicidal morbidity; suicidal risk; suicide rate; tool; volunteer

ABSTRACT. Decades of research to improve the prevention and early detection of suicide risk has largely resulted in the detection of who is most likely to consider suicide, but not when or if that is most likely to happen. Most detection methods presume patients are in contact with the healthcare system, which only reaches a proportion of the at-risk population. Many people at high risk for suicide do not seek professional help because of lack of time, stigma, and fear regarding how they will be treated in the health care system. It is imperative that we develop methods that can identify proximal risk for suicide that does not depend on system- level contact. Web-based search tools are ubiquitous, with 46% of the global population using the internet for information searches and 1.2 trillion searches per year worldwide. Based on our preliminary data, we propose that this online search-engine behavior may prove to be an effective, private, and immediate method of proximal risk detection of suicide for anyone, regardless of their contact with systems of care. We will recruit 1,000 people with mental illness with varying risk for suicide. Participants will provide us access to Google Take-Out (GTO) data, which includes search-engine history and behavior including YouTube. Participants will include those who have report a suicide attempt in the past year (N=500), those who have made an attempt over a year ago (N=250), and those who have thoughts of suicide but never attempted (N=250). All will participate using gold-standard suicide behavior research instruments. Using a case-crossover design, we will evaluate the intermittent exposures (search based proximal risk factors) with an immediate and transient effect on risk and an abrupt outcome (suicide attempt). The case-crossover design is a well-tested and proven approach especially in cases where transient events can trigger acute events such as cardiovascular events, injuries, and death due to environmental exposures and has been studied with interview data to determine warning signs for suicide attempts. Further for predicting suicidal attempt/s, we will use robust ensemble-based machine learning methods such as random forest, gradient boosting to evaluate the predictive nature of qualitative and quantitative features. The study will conclude in a collaborative dissemination planning process with our community partners. Thus, this retrospective and prospective study that aligns GTO data with carefully assessed suicidal thoughts and behaviors has the potential to identify warning signs in search and YouTube data that predict when suicidal risk and lay the groundwork for innovative pathways to suicide prevention.

抽象的。数十年来旨在改善自杀风险预防和早期发现的研究在很大程度上取得了进展 结果发现谁最有可能考虑自杀，但没有发现何时或是否最有可能考虑自杀 发生。大多数检测方法都假设患者与医疗保健系统有联系，而这仅 覆盖一部分高危人群。许多自杀风险较高的人并不寻求专业人士的帮助 由于缺乏时间、耻辱以及对医疗保健系统如何对待他们的恐惧而提供帮助。这是 当务之急是我们开发出能够识别不依赖于系统的近期自杀风险的方法。 水平接触。基于网络的搜索工具无处不在，全球 46% 的人口使用互联网进行搜索 全球每年的信息搜索和 1.2 万亿次搜索。根据我们的初步数据，我们建议 这种在线搜索引擎行为可能被证明是一种有效、私密且直接的方法 对任何人进行自杀的近端风险检测，无论他们是否接触过护理系统。我们将招募 1,000 名具有不同自杀风险的精神疾病患者。参与者将为我们提供访问 Google 的权限 Take-Out (GTO) 数据，包括搜索引擎历史记录和行为（包括 YouTube）。参与者将 包括那些在过去一年中报告过自杀企图的人 (N=500)、那些曾尝试过自杀的人 一年多前（N=250），以及有自杀念头但从未尝试过的人（N=250）。都会 参与使用金标准自杀行为研究工具。使用案例交叉设计，我们将 评估间歇性暴露（基于近端危险因素的搜索），并产生即时和短暂的影响 关于风险和突然的结果（自杀企图）。外壳交叉设计经过充分测试和验证 特别是在瞬时事件可能引发心血管事件等急性事件的情况下， 因环境暴露造成的伤害和死亡，并通过访谈数据进行了研究以确定 自杀企图的警告信号。进一步为了预测自杀企图，我们将使用基于鲁棒的集成 机器学习方法，例如随机森林、梯度提升来评估预测性质 定性和定量特征。该研究将以协作传播规划过程结束 与我们的社区合作伙伴。因此，这项回顾性和前瞻性研究将 GTO 数据与 仔细评估自杀想法和行为有可能在搜索和自杀过程中识别警告信号 YouTube 数据可预测自杀风险，并为创新自杀途径奠定基础 预防。